Inference from complex sample survey data covers the theoretical and empirical properties of various variance estimation strategies (e.g., Taylor series approximation, replicated methods, and bootstrap methods for complex sample designs) and how to incorporate those methods into inference for complex sample survey data. Variance estimation procedures are applied to descriptive estimators and to analysis techniques such as regression, analysis of variance, and analysis of categorical data. Generalized variances and design effects are presented. Methods of model-based inference for complex sample surveys are also examined, and the results are contrasted with the design-based type of inference used as the standard in the course. The course will use real survey data to illustrate the methods discussed in class. Students will learn the use of computer software that takes account of the sample design in estimation. Students will carry out a research and analysis project, using techniques and skills learned during the course. A paper describing the student's research will be submitted at the end of the course, and each student will give a short presentation of his/her findings.